Changes in the mining industry business environment are leading to gradual changes in how the supply chain (from ore extraction at the mine to delivery at customer sites) is managed. Global demand is flattening and available supply is increasing. This means that complex planning business models that were developed in an era of supply “push” need to be altered to accommodate a market reality of demand driven “pull”. This white paper introduces a decision support methodology that results in reduced cost, improved throughput, enhanced quality, and increased profit.

1. Executive summary
Changes in the mining industry business environment
are leading to gradual changes in how the supply chain
(from ore extraction at the mine to delivery at customer
sites) is managed. Global demand is flattening and
available supply is increasing. This means that complex
planning business models that were developed in an
era of supply “push” need to be altered to accommodate
a market reality of demand driven “pull”. This white
paper introduces a decision support methodology that
results in reduced cost, improved throughput, enhanced
quality, and increased profit.
by Daniel Spitty and James Balzary
998-2095-04-08-14AR0

2. Impact of Planning Decision Support Tools on Mining Operations Profitability
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Mining companies utilize many disparate systems and repositories to help facilitate and
simplify their planning and scheduling activities. Decision support systems exist all along the
extraction to delivery “value chain” cycle. Oftentimes these systems operate as silos and lack
of integration makes it difficult to consolidate information. As a result, when one process in
the early extraction stage affects a process further down the line, inefficiencies can occur that
result in higher costs and poor resource optimization (see Figure 1) This “variability” is
characteristic of the complex and dynamic business environment which drives ore extraction
and delivery activity. However variability within mining operations can be much more tightly
controlled through the use of new technologies and methodologies which have recently been
introduced to the marketplace.
Over the last five years, throughput in the supply chain has been the key performance
indicator (KPI) for many mining operations. This phenomenon occurred as a result of an
environment where demand was high and supply was the bottleneck. Now the driving KPIs of
most successful mining operations have evolved to include cost, revenue and profitability.
Therefore the philosophy has changed from one of “produce at any cost” to one of “only
produce if profitable”.
In their 2013 review of the top 40 worldwide mining enterprises PriceWaterhouseCoopers
consultants state the following: “In reaction to shareholder demands and both commodity
price and cost pressures, miners have started to shift their focus. The days of maximising
value by solely increasing production volumes are gone. The future is about managing
productivity and improving efficiencies, both of which have suffered in recent years.”
1
Some mining enterprises have been slow to embrace these new changes. They continue to
prioritize throughput as the most important objective despite the fact that market conditions
are shifting. On the fiscal side, these companies are now reporting lower throughput and high
maintenance activity. This paper illustrates how new methodologies and tools can enable
mining operations to better manage variability in a business environment that requires
dynamically updated information for faster and more accurate decision making.
1
PriceWaterhouseCoopers, “Mine: A Confidence Crisis”, Review of global trends in the mining
industry—201,3 p. 5, 2013
Introduction
Total time available (8760 hours)
Scheduled time (loading %) Loss
Available time Loss
Scheduled non-operating time (hoidays etc.)
365 days x 24 hours
Non-available time (downtime)
Operating time Loss Non-operation time within available time (training, etc. )
Effective operating time Loss Rate losses due to operational and maintenance issues
Production
time
Loss Quality losses (e.g. ineffective blasting)
Figure 1
Summary taken from mining
industry production time case
studies (courtesy of
PriceWaterhouseCoopers)

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Variability exists in all areas of the mining resource-to-market supply chain. It is impacted by
the material attributes of the mine and the timing in which extracted material is being provided
to downstream stakeholders. Often equipment is available for use, yet it still performs below
its capability. This is an example of activity that can be captured as a variance.
For example, a train load car of ore cannot be used until the train arrives and is ready for
loading. Therefore, even though a train car is available, another related activity such as the
unavailability of loading equipment can prevent the train car from being loaded. This
particular variability in performance is due to inefficient coordination issues.
In an integrated supply chain environment characterized by multiple supply sources, complex
processing and transport functions, and multiple loading and distribution points, the number
of decision possibilities is high. This makes it difficult for planners to understand what the
best decision is for the most immediate need.
Defining, capturing, recording, and highlighting where the majority of these variability
instances exist is difficult given the amount of dependencies and relationships within the
resource-to-market mining operation. The demand profile (i.e., spot transactions vs.
fulfillment of long term supply contracts) of the commodity extracted from the mine also
influences the degree of variability the operation must manage. Global events such as
political change and weather patterns are also factors.
Most of today‟s planning systems are ill equipped to manage this degree of variability in any
coordinated way. Planning personnel tend to use separate planning systems for each
particular function and planning horizon. These individual systems allow the planners to
utilize one set of assumptions and parameters that produce only a single plan. In addition,
traditional technologies capable of modeling complex geospatial, quality and quantity
variables used by geologists and mining engineers do not have the functionality to manage
downstream supply chain activities. In addition, traditional supply chain planning and
scheduling tools have no capability to manage niche mining operations processes.
This results in “big picture” vagueness which forces planners to become more conservative in
their planning. The planners respond to this environment by building in “buffer” which is
hidden in their projections. An analogy which helps to illustrate this problem is a common
phenomenon we all experience when planning our air travel. Often people will over
compensate the time required to catch a connecting flight at an international airport. They
factor in the variability of the activities such as the likelihood of the incoming flight arriving on
time, the customs and security clearances, the time between gates and terminals, as well as
historical airline performance and weather. The time allocated for transporting themselves
from point A to point B is higher than the actual time needed due to the consequences of
missing the “deadline”, or in this case, the connecting flight. As a result, the duration of the
entire trip is longer than it should be. This same concept is being used to add buffer to the
planning of activities in all parts of the mining life cycle. Lack of actionable information
creates buffer time in the process.
Planners are often focused on the immediate need within their supply chain function (such as
a mining engineer producing a mine plan, or a logistics planner loading trains). Because of
this they can lead themselves to utilise hard constraints on decision criteria that may not
require such rigidity. This focus on the immediate need can cause future problems which are
not visible to the planner at the time they make their decisions. Thus the planners become
reactive problem solvers as opposed to proactive problem preventers.
Managing
variability
“Most of today’s planning
systems are ill equipped
to manage this degree of
variability in any
coordinated way”.

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Technology now exists that standardizes the approach to planning and scheduling across the
mining operations supply chain functions and time horizons (see Figure 2). These new tools
still allow the uniqueness of each function to be accurately modeled and used as the basis for
optimization.
Consider the example of ROM (Run of Mine) stockpiles. Often companies run a production
push operation to the ROM stockpiles. From the shipping back upstream to the ROM
stockpiles, it is often a pull mechanism that focuses on throughput and fulfillment of the right
product to the right ship at the right time.
With an integrated planning across the supply chain, decisions made at a mine planning
stage can now be tracked all the way through the supply chain to determine the impact on the
fulfillment of shipments or demand. This had previously been difficult, time consuming, and in
some cases not possible in the time frame required.
Consider that a shipment impacted by the change in the mine extraction sequence may be 10
days away. However the decision of what block to extract has to occur in the next 24 hours.
These planning and scheduling horizons are managed by two separate teams with limited
common responsibility regarding KPIs. Such a scenario often results in the wrong product
arriving at the right place at the wrong time. This can now be overcome given that the
planning teams, even if they remain separate, are referencing and viewing the same
information in an integrated, one application representation of the entire supply/demand chain
environment. The outcome is one version of the truth for decision support.
As soon as a variable changes in the model, the downstream effects are mirrored in the
system. Updates are automatically provided on what activities need to be changed by the
planners. These tools also enable new scenarios to be created and updated within a matter
of minutes. Then the teams can collaborate and analyze multiple scenarios to determine the
best decision(s) to make in each area of the supply chain. Such an integrated decision
support system facilitates the ability to embrace variability management due to the
Enablers to
embracing
variability
Figure 2
An integrated model enables
planners to manage a fluid
environment where short
term decisions impact long
term profitability

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improvement in visibility, quicker reaction to environmental changes, and to timings of
shipments at port.
Integrated models provide the basis upon which optimization algorithms can assist planners
to make the best decision. The model maps the complexity of the supply chain over multiple
planning horizons (see Figure 3). This provides visibility to the impact of certain decisions on
other processes. These decision support tools both aid the planner and place into context a
methodology for achieving the overall KPIs of the business.
Most mining supply chain decisions are naturally non-linear in nature. Therefore, applying
linear techniques to solve these problems is a questionable approach. Asset capacities such
as truck tonne kilometers (tKm), crusher throughput, and material process plant residence
times are rarely linear or discrete in nature. If discrete or average inputs are used as
foundation assumptions for a model, then the potential for outputs that are below
expectations is high. The non-linear optimization approach designed into these tools can
explore more accurate and representative possibilities than any person can perform on their
own. These non-linear, techniques can provide counter intuitive solutions that break new
ground and allow planners to challenge the status quo. Answers derived from algrotihms can
allow planners to challenge the natural and inherent buffer that exists in their current planning
assumptions.
Inputs such as availability, performance, and capacities of equipment can now be closely
analyzed. These variables can be applied to a time based, forward-looking calendar.
In the cases where the optimizer provides a solution that a planner may not agree with, the
planners can override the system and lock activities in place. Manual decisions, often a
critical requirement in decision support environments need to be honoured and prioritized.
However, an optimisation process that occurs following a manual decision event requires the
decision to be treated as a constraint, with optimisation only occuring around the manually
derived output.
Figure 3
An example of the integration
of the planning horizons for a
mining enterprise

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Consider an example where an integrated plan already exists and is being executed. Within
this current „live‟ plan there are values assigned to both quantity and quality of a given block
of material to be mined (see Figure 4). This quality and quantity of material is then used in
the planning of downstream activities. In this case, the activities include a storing at a mine
stockpile, process plant feed, train load out, rail service, port in loading, storing at a port
stockpile and ship loading. Once the ore is processed and depositied on the finished product
stockpile, the quality is sampled at the train load out which results in a better estimate of the
target quality attribute (ash in a coal operation could be an example). If the ash level is higher
than what was expected, this more detailed data point is imported into the integrated planning
system. The planner can automatically determine if the existing planned activities for the rail
service are still going to achieve the desired specification outcome. The desired outcome is
that the shipment will still be within the tolerance range for ash when loaded.
This potential quality constraint or violation allows the planner to see if any planned activities
downstream need to change. He can determine what impact the changes to this activity can
have on related activities such as stockpile capacity limitations, vessel nomination
specification, or train unload time. If the actual quality result forces the consignment to be
sent to another stockpile, is the equipment at the port available to perform this task? If the
shipment now needs to source from an alternative stockpile, is the required stock available in
an accessible port area at the required quality with available equipment? And what impact will
this have on the ships that had that stock pegged to its shipment? Will these shipments now
be out of specification tolerance as a result?
Planning and scheduling decision support technology can enable planners to embrace
variability, and to understand the impact that new, dynamic information has on the current
plan. Updates can be executed based on the impact of the change while utilizing available
resources in an efficient manner.
Figure 4
Example of mine material
block data and material
quality attributes

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Variability will forever exist for mining companies in the resource-to-market supply chain.
Rather than struggling to remove the variability, mining companies should be looking to
embrace it, understand it and account for it through better modeling and forward looking
decision making.
Mining environments are becoming increasingly dynamic. The amount of reaction time that is
available for planning teams is decreasing. Credible science is needed to generate the
necessary scenarios required to supplement the decisions made by planners.
New tools and methodologies are available today that help to optimize mining operations
across all the functional areas so that throughput, quality, and profit can be improved.
Daniel Spitty is the Global Capability Development Manager for Schneider Electric's Supply and
Demand Optimization Activity. He holds a Bachelor of Commerce from the University of
Melbourne, Australia and joined SolveIT Software in 2010 from KPMG. Mr. Spitty moved from
Australia to Toronto following Schneider Electric's acquisition of SolveIT Software in 2012. He is
responsible for driving growth of the StruxureWare Supply Chain Operation suite of software
(previously SolveIT Software) in North America. Daniel led the SolveIT team to implement an
Integrated Planning and Optimization Solution (IPOS) in Queensland. He has been active in the
strategic roadmap of IPOS and involved in projects for companies such as for BHP Billiton
Mitsubishi Alliance (BMA) Coal, BHP Billiton Iron Ore, Rio Tinto Iron Ore, Fortescue Metals
Group, Xstrata Coal, Xstrata Copper and Roy Hill Iron Ore.
James Balzary is a qualified Geologist and Software Specialist with an extensive background in
operations management improvement in the mining and resources sector. He has over 19 years
of operational experience in multi-commodity open pit and underground operations, and
enterprise software organisations. He holds a Bachelor of Science with Honours from James
Cook University and has published technical papers to globally leading scientific publications.
©2014SchneiderElectric.Allrightsreserved.
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About the authors